Document clustering based on nonnegative sparse matrix factorization

Authors:
C. F. Yang;Mao Ye;Jing Zhao
Affiliations:
CI Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;CI Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;CI Lab, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 4
Cited 1

Distributional clustering of words for text classification

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Document clustering based on non-negative matrix factorization

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Non-negative Matrix Factorization with Sparseness Constraints

The Journal of Machine Learning Research

Supervised input space scaling for non-negative matrix factorization

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel algorithm of document clustering based on non-negative sparse analysis is proposed. In contrast to the algorithm based on non-negative matrix factorization, our algorithm can obtain documents topics exactly by controlling the sparseness of the topic matrix and the encoding matrix explicitly. Thus, the clustering accuracy has been improved greatly. In the end, simulation results are employed to further illustrate the accuracy and efficiency of this algorithm.